Dyslexia detection from EEG signals using SSA component correlation and Convolutional Neural Networks

TL;DR

This paper proposes a novel EEG-based method for early dyslexia detection using SSA to extract features and CNNs to classify channel correlations, aiming for objective diagnosis applicable to pre-readers.

Contribution

It introduces SSA-based feature extraction combined with CNN classification for EEG signals, enhancing early dyslexia detection beyond traditional behavioral tests.

Findings

SSA components improve feature relevance for classification

CNN achieves high accuracy in dyslexia detection from EEG

Method applicable to pre-readers for early diagnosis

Abstract

Objective dyslexia diagnosis is not a straighforward task since it is traditionally performed by means of the intepretation of different behavioural tests. Moreover, these tests are only applicable to readers. This way, early diagnosis requires the use of specific tasks not only related to reading. Thus, the use of Electroencephalography (EEG) constitutes an alternative for an objective and early diagnosis that can be used with pre-readers. In this way, the extraction of relevant features in EEG signals results crucial for classification. However, the identification of the most relevant features is not straighforward, and predefined statistics in the time or frequency domain are not always discriminant enough. On the other hand, classical processing of EEG signals based on extracting EEG bands frequency descriptors, usually make some assumptions on the raw signals that could cause indormation loosing. In this work we propose an alternative for analysis in the frequency domain based on Singluar Spectrum Analysis (SSA) to split the raw signal into components representing different oscillatory modes. Moreover, correlation matrices obtained for each component among EEG channels are classfied using a Convolutional Neural network.